Project description :
Summary (MAX of 2100 characters with spaces)
This project aims to establish new design methods for robust and efficient automotive navigation and optimal management of a fleet of vehicles in harsh environments. In addition, the project also aims to develop innovative metrics for real-time analysis of dangerous driving behaviour as well as real-time analysis of car accidents in order to significantly improve global safety of Canadian drivers. In general, this research proposes to combine measurements from a high sensitivity GPS receiver with data coming from a self-contained inertial navigation system and other complementary autonomous sensors such as odometers and magnetometers. Moreover, in order to provide an affordable solution, the targeted system will be based exclusively on the use of very low cost sensors. It is expected that this project will help reduce the environmental footprint of motor vehicles in addition to having a significant positive impact on overall vehicle safety. For example, improving vehicle localisation accuracy and robustness in harsh environments can significantly reduce the time to find a stolen or misplaced vehicle, which can have an important impact on Canadian companies’ finances. Furthermore, having a robust and precise solution for monitoring vehicle behaviour can lead to the implementation of a new taxation system based on car usage or on driving behaviour, which according to recent studies, can help reduce vehicle greenhouse gas emissions by up to 10%. In addition, accurate reconstruction of car accidents in real-time allow prediction of specific parameters of an accident scene thus improving reaction time and vehicle safety. The proof-of-concept demonstrator will be evaluated in-laboratory and on-road using simulation equipment and a car test platform under real operating conditions in order to characterize protocols and system performance. The project will contribute to international initiatives for the definition of new standards and contribute to Canadian efforts to reduce greenhouse gas emissions, and create new employment opportunities for the team of highly qualified personnel.
Responsibilities of the candidate:
According to the schedule, the first Ph.D. student will be in charge of the following tasks:
1) 1-11 In-depth literature review on project topics
2) 1-13 Training on Orchid platform and associated tools
3) 1-14 Technical study on vehicles’ embedded sensors and data networks
4) 1-15 Technical study on AC1120S rate table
5) 1-34 Initial system architecture selection
6) 1-35 Study of sensor interconnection and data fusion
7) 1-42 Preliminary modeling of the calibration procedure
8) 1-44 Study of temperature effect on inertial measurements
9) 1-52 Preliminary modeling of the AHRS algorithm
10) 1-54 Study of soft and hard iron effects
11) 1-65 Study of advanced non-linear models
12) 2-41 Modeling of the temperature dependant model
13) 2-51 Modeling of the magnetic disturbance detection / compensation algorithm
14) 2-71 Study of advanced sensor error estimation models
15) 2-73 Mathematical modeling of online calibration algorithms
16) 2-91 Mathematical modeling of advanced non-linear filters
17) 2-92 Simulation of non-linear navigation models
18) 2-93 Tests and validation of the simulated non-linear navigation models
19) 3-11 Real-time development of navigation systems
20) 3-12 Real test setup and planning
21) 3-13 Test / validation of the navigation systems in various scenarios
22) 3-23 A-GNSS integration with navigation algorithms / analysis metrics
23) 3-32 Monitoring data integration with navigation algorithms / analysis metrics
The objective of this student’s project is to develop a robust low-cost integrated navigation system combining a high sensitivity GPS receiver with an inertial measurement unit and other autonomous sensors using advanced non-linear estimation techniques. He will closely collaborate with the other members of the research team and he will use a part of the research done by the Master’s and trainee students in order to fulfill his project’s goals.
The main goals of the first Ph.D. student’s work are the following:
During the first year, the Ph.D. student will first conduct an in-depth literature review on integrated navigation systems. His work will principally focus on the study of various navigation filters including linearized models as well as non-linear ones. He will study and consider all possible options without any limitation to the state of the art techniques. This work will include a very strong theoretical background on algorithms including but not limited to: extended Kalman filter, unscented Kalman filter and particle filter. The Ph.D. student will first conduct simple implementation of the filters (maybe not directly related to the final objective of the project) in order to familiarize himself with these estimation techniques. Following these simple implementations, the Ph.D. candidate will conduct a comprehensive analysis based on the obtained results in order to select the most promising algorithms that will be selected for real implementation.
The second year will be mainly dedicated to the modeling, implementation and integration of the selected advanced navigation filters into the multi-sensor integrated navigation system. During this phase, the Ph.D. student will work closely with graduate students that are also in charge of the development of the non-linear models. First, the selected algorithms will be mathematically modeled and implemented in Matlab / Simulink. These algorithms will then be tested intensively using simulated measurements from tools developed simultaneously by a Master’s student. Folowing, the algorithms will be tested and validated on real sensor measurements. After this validation, the Ph.D. student will focus on the real-time implementation of the algorithms into the Orchid VTADS prototype. In addition, The Ph.D. student will also incorporate the work from the Master’s students regarding sensor calibration into his system. The algorithms will be intensively tested in order to quantify their respective contribution to the system’s performance, accuracy, and robustness in various realistic environments. Finally, while the developed systems will be tested and validated by graduated students and trainees, the Ph.D. student will conduct an important study on online sensor error estimation and calibration in order to develop a complete online calibration method into the developed algorithms.
During the third year, the Ph.D. student will analyze in detail the developed algorithms in order to identify its major weaknesses. Hense, the last year will be mainly dedicated to the identification of major flaws in the current architecture of the system and its possible improvement. During this phase, the Ph.D. candidate will return to research in order to explore new approaches for improving this architecture (Add / Remove sensors, sensors redundency, addition of non-holonomic constraints, etc.). He will first validate these approaches in simulation using Matlab / Simulink and then test it on the real system in order to quantify their contribution to the navigation solution in realistic environments.
